Monitoring parameters measured in or on the body of humans such as a concentration of a certain substance in a given body fluid has many applications. In particular, the monitoring is crucial in the context of therapies that involve an administration of active substances regulated depending on the value of one or a plurality of physiological parameters. A prominent example is diabetes therapy where the administration of insulin is effected depending on a measured glucose concentration in a body fluid of the patient.
Conventionally, diabetic patients who need to regularly administer insulin have periodically taken measurements of their blood glucose level, e.g. using a hand held strip-based glucose meter. However, the small number of measurements (usually four a day) provide only a very coarse picture of the progression of the glucose level (“snapshots” in time). They cannot give dynamic information about the metabolic response to a specific event such as a meal or physical activities of the patient; or more generally, the glucose trend during a period of time.
Continuous glucose monitoring (CGM) is a new technology for diabetes self-management. Instruments for continuous glucose monitoring record glucose concentrations over a period of time that lasts from several hours to several days, weeks or even months. The measurement frequency is much higher than that of the traditional spot blood glucose (bG) measurements referred to above (usually at least 10 measurements per hour). In principle, the increased temporal resolution provides the patient as well as his or her health care provider(s) (HCP) with a rich data set of time-variant glucose information. In principle, the continually measured glucose data can be used to more specifically adjust and refine the diabetes therapy to individual needs by adjusting the basal insulin rate as well as the timing and the amount of boluses. Furthermore, the data provides indications about advisable changes of the patient's behavior, e.g. concerning different food choices (type, portion) or activity changes.
However, there are several reasons why people with diabetes struggle to gain maximal benefit from Continuous Glucose Monitoring. First, a raw glucose signal over time can be complicated to understand. Continuous glucose monitoring is a data-intensive diagnostic tool and can therefore cause the user to become overwhelmed by an overload of information for which they have no use or explanation.
It is known to provide the user of CGM equipment with simplified real-time features such as trend arrows and hypo alarms. However, these features fail to give patients the “big picture” needed for deeper learning and behavior modification.
US 2005/004439 A1 (Medtronic MiniMed) relates to glucose monitoring systems and in particular to calibration methods for such systems. The calibration process involves obtaining glucose monitor data at predetermined intervals over a period of time as well as obtaining at least two reference glucose values from a reference source (e.g. a blood glucose meter) that correspond with the obtained glucose monitor data; starting from the corresponding data, calibration characteristics are calculated, which are subsequently used for calibrating the obtained glucose monitor data. The received data, i.e. the blood glucose history, may be analyzed, displayed and logged. A software is used to download the data, create a data file, calibrate the data, and display the data in various formats including charts, forms, reports, graphs, tables, lists, and the like. The displayed information includes trending information of the characteristic (e.g., rate of change of glucose), graphs of historical data, average characteristic levels (e.g., glucose), stabilization and calibration information, raw data, tables (showing raw data correlated with the date, time, sample number, corresponding blood glucose level, alarm messages, and more), and the like.
US 2003/125612 A1 (J. Kelly Fox et al.) relates to medical monitoring systems, in particular to blood glucose monitoring of diabetic people. The described system allows for performing predictive analyses to anticipate harmful conditions, such as hyperglycemic incidents. This process may involve repeatedly measuring the respective physiological value to obtain a series of physiological characteristic values to determine how the physiological characteristic is changing over time. Furthermore, the process may involve the extrapolation of curves, the calculation of averages of the series of physiological characteristic values or the calculation of line fits, e.g. over a defined span of time (e.g. one hour). The described systems aim at providing meaningful retrospective information to the patient using the sensor and at conveniently and efficiently storing and displaying such useful information. For this purpose the collected data may retrospectively be displayed in the form of a minimum and maximum data presentation, as an excursion data presentation, as a characteristic value distribution data presentation or as an integrated characteristic value data presentation.
WO 00/19887 A1 (Minimed) relates to telemetered subcutaneous sensor devices featuring wireless communication between an implantable subcutaneous sensor set, e.g. for measuring blood glucose, and a remotely located monitor. The monitor displays and logs the received glucose readings. The information displayed on the display of the monitor may include trending information of the characteristic (e.g., rate of change of glucose), graphs of historical data, average characteristic levels (e.g., glucose), or the like.
US 2002/002326 A1 (Minimed) relates to remote programs and/or handheld personal assistants (PDAs) for use with medical devices. The information displayed on the display of the monitor may include trending information of the characteristic (e.g., rate of change of glucose), graphs of historical data, average characteristic levels (e.g., glucose), or the like. Depending on the actual embodiment, the raw received sensor signals or calibrated or adjusted results may be stored for downloading, later analysis or review. US 2006/031094 A1 (Medtronic MiniMed) relates to systems and processes for managing data relating to medical or biological conditions of a plurality of subjects (e.g. diabetic subjects) over a wide area network. A corresponding system is realized by a group of software modules running on one or more servers connected to the wide-area network; the users may communicate with the medical data management system over the internet, whereas subject support devices (such as e.g. meters or biological sensors) may be connected to user-side computers. A subject support device, such as an infusion pump, may communicate with a plurality of meters or sensors (e.g. by wireless interfaces) and store information received from these further devices for later communication over the wide area network. Further information may be provided manually by the user by entering into the subject-side computer or the subject support device, e.g. information relating to a subject's activity, such as dietary information, eating times and amounts, exercise times and amounts, or the like. The system features a database layer that may include a centralized database repository that is responsible for warehousing and archiving stored data in an organized format for later access, and retrieval. The centrally stored data may be employed to analyze historical information regarding a subject's biological condition, operation of the subject support devices, treatment, personal habits, etc. A reporting layer may include a report wizard program that pulls data from selected locations in the database and generates report information from the desired parameters of interest. Reports may have the form of bar graphs, x-y coordinate graphs, pie charts, scatter charts, stacked bar charts, etc.
However, during retrospective analysis there is the common problem that the patient has already forgotten the specific circumstances that accompanied or caused a certain marked effect on the glucose level. In principle, maintaining a day-to-day log book is a solution to this problem, but requires considerable discipline.
Furthermore, if there is a multitude of different ways of displaying the (processed) information, many people are dependent on their HCP for interpreting the displayed data. Due to these reasons most people cannot fully benefit from CGM today because they must rely largely on HCPs for retrospective analysis and guidance on CGM data.